{"id":"W2079529928","doi":"10.1007/s11306-012-0482-9","title":"Translational biomarker discovery in clinical metabolomics: an introductory tutorial","year":2012,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":957,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Canadian Institutes of Health Research; Genome Alberta; Genome Canada","keywords":"Biomarker; Receiver operating characteristic; Metabolomics; Context (archaeology); Biomarker discovery; Computer science; Machine learning; Computational biology; Bioinformatics; Proteomics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00224463,0.0003418938,0.0006039091,0.0001743906,0.00009676026,0.00005577717,0.0003452911,0.0003188888,0.00005691782],"category_scores_gemma":[0.0003729849,0.0003165461,0.0002881568,0.0002324159,0.0002132414,0.00007482503,0.0001557617,0.0002852141,0.00002344382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000199074,"about_ca_system_score_gemma":0.0001212984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002336934,"about_ca_topic_score_gemma":0.00006293012,"domain_scores_codex":[0.9972172,0.0003993312,0.000829791,0.000645077,0.0002318818,0.0006766927],"domain_scores_gemma":[0.9988391,0.00005570103,0.0001877106,0.0006302439,0.00007737924,0.0002098182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001359355,0.001624937,0.4116641,0.00002257624,0.0007034712,0.000003172706,0.0002530277,0.00005683956,0.5361658,0.03130572,0.003633831,0.01320715],"study_design_scores_gemma":[0.002617506,0.0001704701,0.5030833,0.000003466459,0.0001725147,0.0000145083,0.0001543479,0.0001096787,0.02206262,0.0006271903,0.4702742,0.000710151],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815098,0.01056538,0.001894149,0.0001911131,0.004823487,0.0002667862,0.0001006621,0.00002054076,0.0006280644],"genre_scores_gemma":[0.9840751,0.002619823,0.006777568,0.0003390091,0.005344426,0.00003545904,0.0003306665,0.00005425061,0.0004236624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5141032,"threshold_uncertainty_score":0.9999287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02978484928780126,"score_gpt":0.3152301164199134,"score_spread":0.2854452671321121,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}